Choose Workflows or Cloud Composer for service orchestration
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BothWorkflowsandCloud Composercan be used for service
orchestration to combine services to implement application functionality or
perform data processing. Although they are conceptually similar, each is
designed for a different set of use cases. This page helps you choose the right
product for your use case.
Key differences
The core difference between Workflows and Cloud Composer
is what type of architecture each product is designed to support.
Workflowsorchestrates multiple HTTP-based services into a
durable and stateful workflow. It has low latency and can handle a high number
of executions. It's also completely serverless.
Workflows is great for chaining microservices together,
automating infrastructure tasks like starting or stopping a VM, and integrating
with external systems. Workflows connectors also support simple
sequences of operations in Google Cloud services such as Cloud Storage
and BigQuery.
Cloud Composeris designed to orchestrate data driven workflows
(particularly ETL/ELT). It's built on the Apache Airflow project, but
Cloud Composer is fully managed. Cloud Composer supports your
pipelines wherever they are, including on-premises or across multiple cloud
platforms. All logic in Cloud Composer, including tasks and scheduling,
is expressed in Python as Directed Acyclic Graph (DAG) definition files.
Cloud Composer is best for batch workloads that can handle a few
seconds of latency between task executions. You can use Cloud Composer
to orchestrate services in your data pipelines, such as triggering a job in
BigQuery or starting a Dataflow pipeline. You can use
pre-existing operators to communicate with various services, and there are over
150 operators for Google Cloud alone.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-09-04 UTC."],[],[],null,["# Choose Workflows or Cloud Composer for service orchestration\n\nBoth [Workflows](/workflows/docs/overview) and\n[Cloud Composer](/composer/docs/concepts/overview) can be used for service\norchestration to combine services to implement application functionality or\nperform data processing. Although they are conceptually similar, each is\ndesigned for a different set of use cases. This page helps you choose the right\nproduct for your use case.\n\nKey differences\n---------------\n\nThe core difference between Workflows and Cloud Composer\nis what type of architecture each product is designed to support.\n\n**Workflows** orchestrates multiple HTTP-based services into a\ndurable and stateful workflow. It has low latency and can handle a high number\nof executions. It's also completely serverless.\n\nWorkflows is great for chaining microservices together,\nautomating infrastructure tasks like starting or stopping a VM, and integrating\nwith external systems. Workflows connectors also support simple\nsequences of operations in Google Cloud services such as Cloud Storage\nand BigQuery.\n\n**Cloud Composer** is designed to orchestrate data driven workflows\n(particularly ETL/ELT). It's built on the Apache Airflow project, but\nCloud Composer is fully managed. Cloud Composer supports your\npipelines wherever they are, including on-premises or across multiple cloud\nplatforms. All logic in Cloud Composer, including tasks and scheduling,\nis expressed in Python as Directed Acyclic Graph (DAG) definition files.\n\nCloud Composer is best for batch workloads that can handle a few\nseconds of latency between task executions. You can use Cloud Composer\nto orchestrate services in your data pipelines, such as triggering a job in\nBigQuery or starting a Dataflow pipeline. You can use\npre-existing operators to communicate with various services, and there are over\n150 operators for Google Cloud alone.\n\nDetailed feature comparison\n---------------------------\n\n*** ** * ** ***\n\n1. [Source code for airflow.models.xcom](https://airflow.apache.org/docs/apache-airflow/stable/_modules/airflow/models/xcom.html).\n *Apache Airflow documentation* . August 2, 2021. [↩](#fnref1)"]]
